Showing papers on "Cognitive network published in 2020"

Secrecy Performance Analysis of UAV Assisted Relay Transmission for Cognitive Network With Energy Harvesting

Abstract: The unmanned aerial vehicle (UAV) assisted transmission can significantly improve the spectrum efficiency and coverage of the wireless communication network. The paper proposed a cooperative secrecy transmission mechanism, which can take advantage of transmitter signal of primary user (PU) as a dedicated radio frequency (RF) source with decode and forward (DF) UAV selection and energy harvesting (EH) under cognitive network. Specifically, the transmitting signal of PU in the cognitive network can be viewed and used as energy to drive secondary user (SU) nodes and multi-relay nodes to send signals. It was worth noting that the DF UAV relay may bring about the unexpected noise and decrease the received signal to noise ratio (SNR), therefore, the multiple UAV assisted relays selected with secrecy capacity maximization criteria under cognitive network was proposed and investigated in detail, where the destination node equipped with multiple antennas and adopted the optimal antenna selection receiver. Moreover, By using the function analysis method, the closed expressions of the system secrecy outage probability (SOP) and the probability of non-zero secrecy capacity were calculated and derived accurately. Furthermore, the asymptotic expressions of the UAV assisted relay system under the large SNR cases were analyzed in detail, where the expressions of secrecy diversity order and secrecy diversity gain of the UAV assisted relay cognitive system were obtained thoroughly. Simulation results verified the effectiveness of the proposed mechanism and the correctness of the calculation.

AI-driven Zero-touch Operations, Security and Trust in Multi-operator 5G Networks: a Conceptual Architecture

Abstract: The 5G network solutions currently standardised and deployed do not yet enable the full potential of pervasive networking and computing envisioned in 5G initial visions: network services and slices with different QoS profiles do not span multiple operators; security, trust and automation is limited. The evolution of 5G towards a truly production-level stage needs to heavily rely on automated end-to-end network operations, use of distributed Artificial Intelligence (AI) for cognitive network orchestration and management and minimal manual interventions (zero-touch automation). All these elements are key to implement highly pervasive network infrastructures. Moreover, Distributed Ledger Technologies (DLT) can be adopted to implement distributed security and trust through Smart Contracts among multiple non-trusted parties. In this paper, we propose an initial concept of a zero-touch security and trust architecture for ubiquitous computing and connectivity in 5G networks. Our architecture aims at cross-domain security & trust orchestration mechanisms by coupling DLTs with AI-driven operations and service lifecycle automation in multi-tenant and multi-stakeholder environments. Three representative use cases are identified through which we will validate the work which will be validated in the test facilities at 5GBarcelona and 5TONIC/Madrid.

Cognitive Similarity-Based Collaborative Filtering Recommendation System

Abstract: This paper provides a new approach that improves collaborative filtering results in recommendation systems In particular, we aim to ensure the reliability of the data set collected which is to collect the cognition about the item similarity from the users Hence, in this work, we collect the cognitive similarity of the user about similar movies Besides, we introduce a three-layered architecture that consists of the network between the items (item layer), the network between the cognitive similarity of users (cognition layer) and the network between users occurring in their cognitive similarity (user layer) For instance, the similarity in the cognitive network can be extracted from a similarity measure on the item network In order to evaluate our method, we conducted experiments in the movie domain In addition, for better performance evaluation, we use the F-measure that is a combination of two criteria P r e c i s i o n and R e c a l l Compared with the Pearson Correlation, our method more accurate and achieves improvement over the baseline 111% in the best case The result shows that our method achieved consistent improvement of 18% to 32% for various neighborhood sizes in MAE calculation, and from 20% to 41% in RMSE calculation This indicates that our method improves recommendation performance

Performance Analysis for Multihop Cognitive Radio Networks With Energy Harvesting by Using Stochastic Geometry

Abstract: Cognitive multihop relaying has been widely considered for device-to-device (D2D) communications for applications in the physical layer of the Internet of Things. In this article, we construct a multihop cellular D2D communications system model with energy harvesting (EH) in underlay cognitive radio networks. The locations of primary user equipments (PUEs) and cellular base stations are considered as a Poisson point process in this model. The transmit power of secondary devices is collected from the power beacon with time-switching EH policy. Two charging policies for different applications are considered in this article. Then, the end-to-end outage probability analysis expressions of these two scenarios for the transmission scheme subject to interferences from PUEs are derived. The optimal harvesting time ratio is obtained to get the maximum capacity for end-to-end D2D communications. The analytical results are validated by performing the Monte Carlo simulation of the end-to-end outage probability, which is based on the half-duplex transmission scheme. The results of this article provide a potential pathway to reduce reliance on grid or battery energy supplies and, hence, further strengthen the benefits for the environment and deployment of future smart devices.

Full-Duplex Non-Orthogonal Multiple Access Cooperative Spectrum-Sharing Networks With Non-Linear Energy Harvesting

Abstract: In this paper, we analyze the performance of a non-orthogonal multiple access (NOMA) cognitive relay system, where a multi-antenna full-duplex cognitive transmitter employs NOMA concept to assist the transmission from a wireless-powered primary transmitter to its corresponding receiver, while simultaneously communicating with a cognitive receiver. A practical non-linear energy harvesting (EH) model, which taking into account harvester's sensitivity and saturation effects is considered. We propose an optimum beamforming design at the cognitive transmitter such that the rate of cognitive network is maximized, under a constraint that the rate of the primary network is above a certain threshold. Results show that proposed optimization framework can substantially enlarge the rate region toward both primary and secondary networks. Furthermore, in order to characterize the network delay-constrained throughput, tractable outage probability expressions for primary and secondary networks assuming sub-optimum zero-forcing based beamforming scheme are derived. Our results reveal that due to required minimum power for harvesting operation as well as saturation of the harvested power at high transmit power levels, conventional linear EH model may lead to performance mismatches for practical non-linear EH circuits in both low and high transmit power regimes.

Joint D2D Assignment, Bandwidth and Power Allocation in Cognitive UAV-Enabled Networks

Abstract: This paper considers a cognitive communication network, which consists of a flying base station deployed by an unmanned aerial vehicle (UAV) to serve its multiple downlink ground terminals (GTs), and multiple underlaid device-to-device (D2D) users. To support the GTs’ throughput while guaranteeing the quality-of-service for the D2D users, the paper proposes the joint design of D2D assignment, bandwidth, and power allocation. This design task poses a computationally challenging mixed-binary optimization problem, for which a new computational method for its solution is developed. Multiple binary (discrete) constraints for the D2D assignment are equivalently expressed by continuous constraints to leverage systematic processes of continuous optimization. As a result, this problem of mixed-binary optimization is reformulated by an exactly penalized continuous optimization problem, for which an alternating descent algorithm is proposed. Each round of the algorithm invokes two simple convex optimization problems of low computational complexity. The theoretical convergence of the algorithm can be easily proved and the provided numerical results demonstrate its rapid convergence to an optimal solution. Such a cognitive network is even more desirable as it outperforms a non-cognitive network, which uses a partial bandwidth for D2D users only.

Routing With Intelligent Spectrum Assignment in Full-Duplex Cognitive Networks Under Varying Channel Conditions

Abstract: Recent developments of self-interference suppression techniques have enabled practical implementations of full-duplex (FD) cognitive-radio (CR) communication systems. Such systems can significantly enhance spectrum utilization by allowing a CR user to simultaneously transmit and receive over the same frequency channel. However, the CR FD-capabilities challenge the effectiveness of existing CR-based routing protocols as joint FD-aware channel-assignment and routing are essential to enhance network performance. In this letter, we propose a joint FD-aware channel-assignment and route selection protocol in FD-based CR networks (CRNs) under time-varying channel conditions and transmission rates. Specifically, for a given set of paths between a CR source-destination pair, our protocol computes the channel-assignment over each path that maximizes the end-to-end network throughput subject to interference constraints. This assignment problem is shown to be an NP-hard binary linear programming (BLP) that can be sub-optimally solved in polynomial-time using the sequential-fixing procedure. Then, our protocol determines the path with the highest end-to-end network throughput. Simulation results reveal that our proposed routing protocol significantly improves the end-to-end throughput compared to previous FD-aware routing protocols.

Intelligent Secure Communication for Cognitive Networks With Multiple Primary Transmit Power

Abstract: In this paper, we study an intelligent secure communication scheme for cognitive networks with multiple primary transmit power, where a secondary Alice transmits its secrecy data to a secondary Bob threatened by a secondary attacker. The secondary nodes limit their transmit power among multiple levels, in order to maintain the quality of service of the primary networks. The attacker can work in an eavesdropping, spoofing, jamming or silent mode, which can be viewed as the action in the traditional Q-learning algorithm. On the other hand, the system can adaptively choose the transmit power level among multiple ones to suppress the intelligent attacker, which can be viewed as the status of Q-learning algorithm. Accordingly, we firstly formulate this secure communication problem as a static secure communication game with Nash equilibrium (NE) between the main links and attacker, and then employ the Q-learning algorithm to select the transmit power level. Simulation results are finally demonstrated to verify that the intelligent attacker can be effectively suppressed by the proposed studies in this paper.

Capacity and Frequency Optimization of Wireless Backhaul Network Using Traffic Forecasting

Abstract: Telecom operators are aiming to provide high-grade data, multimedia applications and low latency videos for smart devices. As today’s mobile data is experiencing rapid growth and the usage of smart devices are fabricating unparalleled challenges for telecom operators to meet the global bandwidth requirement. From the first generation to fourth generation, the technology evolution is predominantly governed by the hardware side but now it is moving towards the concept of cognitive network management, resource orchestration and machine learning-based solutions. In this paper, we propose the adaptive capacity and frequency optimization (ACFO) method for adaptive optimization based on time series forecasting approach. The daily capacity utilization of microwave (MW) links is analyzed to use forecasted demand. Based on the projected demand, the capacity and frequency optimization will be executed. The two main forecasting models 1) SARIMA and 2) MLP are used and for performance evaluation, we used RMSE and MAPE criterion. The analytic outcomes show that MLP with two layers and six hidden nodes (6/6) are good enough to achieve the desired results. In some cases, we need to exceed the hidden nodes up to fifteen (15/15). By using the forecasting approach, the reactive optimization will successfully shift to the predicted/proactive optimization, will balance the resource distribution and can condense the wastage of resources. The outcome of the study will be a contribution to the dynamic resource optimization in wireless backhaul network.

Nonsynaptic Error Backpropagation in Long-Term Cognitive Networks

Abstract: We introduce a neural cognitive mapping technique named long-term cognitive network (LTCN) that is able to memorize long-term dependencies between a sequence of input and output vectors, especially in those scenarios that require predicting the values of multiple dependent variables at the same time. The proposed technique is an extension of a recently proposed method named short-term cognitive network that aims at preserving the expert knowledge encoded in the weight matrix while optimizing the nonlinear mappings provided by the transfer function of each neuron. A nonsynaptic, backpropagation-based learning algorithm powered by stochastic gradient descent is put forward to iteratively optimize four parameters of the generalized sigmoid transfer function associated with each neuron. Numerical simulations over 35 multivariate regression and pattern completion data sets confirm that the proposed LTCN algorithm attains statistically significant performance differences with respect to other well-known state-of-the-art methods.

Optimal Power Control for Cognitive LEO Constellation With Terrestrial Networks

Abstract: Power control plays a significant role in cognitive networks, which promotes the spectrum sharing between heterogeneous systems. This letter presents the mathematical model for cognitive low earth orbit (LEO) satellite constellation with terrestrial networks, which takes the dynamic characteristics of the LEO satellite into consideration. Two optimal power control schemes are proposed from the long-term and short-term perspectives, which aims to maximize the delay-limited capacity and minimize the outage probability, respectively. Solutions to the optimization problems are analyzed, and numerical results evaluate the performance of the schemes.

Energy-Efficient Cooperative Spectrum Sensing in Cognitive Satellite Terrestrial Networks

Abstract: Having the ability to provide seamless coverage and alleviate the frequency scarcity, the cognitive satellite terrestrial network becomes a promising candidate for future communication networks. In the cognitive network, spectrum sensing plays an important role in detecting the channel state for opportunistic utilization, where cooperative spectrum sensing is employed to improve the sensing performance. Additionally, it is critical for battery-powered satellite mobile terminals to diminish energy consumption costs. In this regard, this paper proposes a novel sensing-based cognitive satellite terrestrial network (SCSTN), which integrates the cognitive satellite terrestrial network with the distributed cooperative spectrum sensing network. Specifically, we focus on energy-efficient cooperative sensing in the SCSTN, which maximizes the energy efficiency (EE) of the cognitive satellite network by a tradeoff between the average throughput and the average energy consumption. In the SCSTN, the energy detection threshold of the sensing node and the rule threshold of fusion affect the average throughput and the average energy consumption. Hence, the objective of this paper is to identify the energy detection threshold of the sensing node and the rule threshold of fusion to achieve the maximum EE. We first study the EE formulation of the rule threshold of fusion when the energy detection threshold of the sensing node is given, and transform the ratio-type objective function of EE into a parametric formulation. Subsequently, by exploring the relationship between the two formulations and making use of the monotonicity of the parametric formulation, an algorithm to obtain the optimal rule threshold of fusion for the original problem is developed. Furthermore, we study the optimal formulation of the energy sensing threshold of the sensing node and discuss the effect of the sensing duration and the number of distributed cooperative terminals on the EE. Lastly, the performance of the proposed method is evaluated through numerical simulation results.

Introduction into cognitive multimodal transportation systems

Abstract: The article considers the intellectualization basics with regard to multimodal transportation systems and strategies for its further development. The authors define the concept of the cognitive cycle of a transportation system, present its structure, and determine specifics of its use for the implementation of cognitive multimodal transportation systems. Primary components of an intelligent transportation system are identified through the example of a motor transport subsystem. The authors also consider specifics of forming the architecture of cognitive network transport infrastructures, their stratified and procedural representation.

The Adversarial Machine Learning Conundrum: Can the Insecurity of ML Become the Achilles' Heel of Cognitive Networks?

Abstract: The holy grail of networking is to create cognitive networks that organize, manage, and drive themselves. Such a vision now seems attainable thanks in large part to the progress in the field of machine learning (ML), which has now already disrupted a number of industries and revolutionized practically all fields of research. But are the ML models foolproof and robust to security attacks to be in charge of managing the network? Unfortunately, many modern ML models are easily misled by simple and easily-crafted adversarial perturbations, which does not bode well for the future of ML-based cognitive networks unless ML vulnerabilities for the cognitive networking environment are identified, addressed, and fixed. The purpose of this article is to highlight the problem of unsecure ML and to sensitize the readers to the danger of adversarial ML by showing how an easily crafted adversarial ML example can compromise the operations of the cognitive self-driving network. In this article, we demonstrate adversarial attacks on two simple yet representative cognitive networking applications (namely, intrusion detection and network traffic classification). We also provide some guidelines to design secure ML models for cognitive networks that are robust to adversarial attacks on the ML pipeline of cognitive networks.

Spectrum sensing and energy detection in cognitive networks

Abstract: It is worth mentioning that the use of wireless systems has been increased in recent years and supposed to highly increase in the few coming years because of the increasing demands of wireless applications such as mobile phones, Internet of Things (IoT), wireless sensors networks (WSNs), mobile applications and tablets. The scarcity of spectrum needs to be into consideration when designing a wireless system specially to answer the two following questions; how to use efficiently the spectrum available for the available networks in sharing process and how to increase the throughput delivered to the serving users. The spectrum sharing between several types of wireless networks where networks are called cognitive networks is used to let networks cooperate with each other by borrowing some spectrum bands between them especially when there is an extra band that is not used. In this project, the simulation of spectrum sensing and sharing in cognitive networks is performed between two cognitive networks. This project discusses the performance of probability of energy detected (Pd) with different values of false alarm (Pf) and Signal-To-Noise Ratio (SNR) values to evaluate the performance of the sensing and sharing process in cognitive networks. The results show that when the request of sharing spectrum increased, the full sharing process occurs for a long time and the error rate decreases for small values of SNR.

Radio environment maps for military cognitive networks: density of small-scale sensor network vs. map quality

Abstract: In this paper, we present the dependency between density of a sensor network and map quality in the radio environment map (REM) concept. The architecture of REM supporting military communications systems is described. The map construction techniques based on spatial statistics and transmitter location determination are presented. The problem of REM quality and relevant metrics are discussed. The results of field tests for UHF range with a different number of sensors are shown. Exemplary REM maps with different interpolation algorithms are presented. Finally, the problem of density of a sensor network versus REM map quality is analyzed.

Reliable and Secure Transmission in Multiple Antennas Hybrid Satellite-Terrestrial Cognitive Networks Relying on NOMA

Abstract: We study a hybrid satellite-terrestrial cognitive network (HSTCN) relying on non-orthogonal multiple access (NOMA) interconnecting a satellite and multiple terrestrial nodes. In this scenario, the long distance communication is achieved by the satellite equipped multiple antennas to send information to a multi-antenna destinations through the base station acting as relay. The secure performance is necessary to study by exploiting the appearance of an eavesdropper attempting to intercept the transmissions from relay to destinations. We explore situation of hardware imperfections in secondary network and deign of multiple antennas need be investigated in term of the physical-layer security by adopting the decode-and-forward (DF) relay strategy. Specifically, we guarantee coverage area by enabling relaying scheme and keep outage probability (OP) performance satisfying required data rates. Moreover, suppose that only the main channels’ state information is known while the wiretap channels’ state information is unavailable due to the passive eavesdropper, we analyze the secrecy performance in term of intercept probability (IP) of the HSTCN by driving the closed-form expressions of such performance metric. Finally, the presented simulation results show that: 1) The outage behaviors of NOMA-based HSTCN network does not depend on transmit signal to noise ratio (SNR) at source at high SNR; 2) Numerical results show that the such system using higher number of transceiver antennas generally outperform the system with less antennas in terms of OP and IP and reasonable selection of parameters is necessary to remain the secrecy performance of such systems; and 3) By allocating different power levels to tow users, the second user has better secure behavior compared with the first user regardless of other set of satellite links or the number of antennas, which means that the superiority of the second user compared with user the first user in terms of OP and IP are same.

Fuzzy-Rough Cognitive Networks: Theoretical Analysis and Simpler Models.

Abstract: Fuzzy-rough cognitive networks (FRCNs) are recurrent neural networks (RNNs) intended for structured classification purposes in which the problem is described by an explicit set of features. The advantage of this granular neural system relies on its transparency and simplicity while being competitive to state-of-the-art classifiers. Despite their relative empirical success in terms of prediction rates, there are limited studies on FRCNs' dynamic properties and how their building blocks contribute to the algorithm's performance. In this article, we theoretically study these issues and conclude that boundary and negative neurons always converge to a unique fixed-point attractor. Moreover, we demonstrate that negative neurons have no impact on the algorithm's performance and that the ranking of positive neurons is invariant. Moved by our theoretical findings, we propose two simpler fuzzy-rough classifiers that overcome the detected issues and maintain the competitive prediction rates of this classifier. Toward the end, we present a case study concerned with image classification, in which a convolutional neural network is coupled with one of the simpler models derived from the theoretical analysis of the FRCN model. The numerical simulations suggest that once the features have been extracted, our granular neural system performs as well as other RNNs.

Security Enhancement for Energy Harvesting Cognitive Networks with Relay Selection

Abstract: Relay selection is proposed in this paper as an efficient solution to secure information transmission of secondary users against eavesdroppers in energy harvesting cognitive networks. The proposed relay selection method selects a secondary relay among available secondary relays, which are capable of harvesting radio frequency energy in signals of the secondary transmitter and correctly restore secondary message, to curtail signal-to-noise ratio at the wire-tapper. In order to evaluate the security performance of the suggested relay selection method, an exact intercept outage probability formula accounting for peak transmit power confinement, Rayleigh fading, and interference power confinement is firstly derived. Monte-Carlo simulations are then generated to corroborate the proposed formula. Numerous results expose that positions of relays, the number of relays, and parameters of the energy harvesting method significantly influence the security performance while the power confinements on secondary transmitters cause the performance saturation.

Nash Bargaining-Based Economic Analysis of Opportunistic Cognitive Cellular Networks

Abstract: We analyse a cognitive cellular network comprising of a primary and a secondary network operator (PNO and SNO). The SNO implements distributed detection to opportunistically access the licensed band. In return, the PNO requests the SNO to pay a remuneration. The PNO judiciously balances between the revenue gained from its subscribers and the payment earned from the SNO. On the other hand, the SNO must earn a positive utility to maintain its infrastructure. Consequently, a two-person non-cooperative bargaining game is formulated where the operators agree upon the value of the following factors: base station activity probabilites and the payment from the SNO. The PNO is aware of detection and false-alarm probabilities of spectrum sensing by the SNO and hence can infer how the interference created by missed detection degrades its capacity. We establish that our formulated game has a unique Nash solution. For linear demands, we derive an approximate closed-form solution. Numerical analysis reveals several insightful results. For example, we exhibit that, if the user number of the SNO lies below a certain threshold, the cognitive network appears to be economically unsustainable. We also show the functional dependence of the bargaining outcome on various system parameters.

Applications of Machine Learning in Optical Communications and Networks

Abstract: We discuss various applications of machine learning techniques in different aspects of optical communications and networking including optical performance monitoring, fiber nonlinearity compensation, cognitive network failure prediction, dynamic planning and cross-layer optimization of software-defined networks, quality of transmission estimation, and physical layer design of optical communication systems. Recent works employing deep learning technologies are also discussed.

A metaheuristic optimization model for spectral allocation in cognitive networks based on ant colony algorithm (M-ACO)

Abstract: Cognitive radio networks have been gaining widespread attraction among researchers especially with the increasing demand for radio frequency spectrum whose availability is quite scarce. Cognitive radio networks provide an ideal solution to allocate spectrum to users on an intelligent basis through a series of spectrum sensing and decision making. A metaheuristic soft computing framework is proposed and implemented in this research work by using powerful optimization concepts of evolutionary algorithm, namely ant colony algorithm, coupled with graph-cut modeling of given wireless network to provide the expected precision of detection. Channel characteristics have been taken as the feature vectors which are modeled as n-tuple graph to decide upon the maximization of channel allocation probability based on availability in an opportunistic basis. Exhaustive experimentations have been conducted and optimal performance justified against other benchmark algorithms.

An Intelligent Stock Trading Decision Support System based on Rough Cognitive Reasoning

Abstract: From the perspective of Momentum Investing (MI), more profitable trading opportunities for bullish investors would exist in the stocks occurring with limit-up. Motivated by this, we propose an intelligent stock trading decision support system by using rough cognitive reasoning, based on which stocks with the higher probabilities of rising in the short term after the occurrences of limit-up can be distinguished. Considering financial markets are full of uncertainty and high noise, an extended rough cognitive network (RCN) is established, which is a granular reasoning model based on rough set theory and fuzzy cognitive maps. As a kind of reasoning mechanism, the extended RCN can effectively analyze both the continuous and discrete features of financial data to deal with the uncertainty and inconsistency. Moreover, entropy-based method is involved into the extended RCN model such that the knowledge representation of model can be further improved, and harmony search algorithm is applied for optimization. The proposed model is further applied in Chinese stock market to carry out empirical studies, where the discussion on parameters are implemented and experiment results show the effectiveness and validity of the proposed model.